Nearly 60,000 patients diagnosed with prostate cancer (CaP) in the United States undergo radical prostatectomy (RP) each year. For 15-40% of these patients, biochemical recurrence (BCR) of the prostate cancer occurs within five years of surgery. Gleason scoring is a pathological grading system based on visual analysis by a pathologist of glandular and nuclear morphology. Gleason scoring is currently regarded as the best biomarker for predicting CaP aggressiveness and long-term post-surgical patient outcome. However, the post-surgical outcome of CaP patients with the same intermediate Gleason scores can vary significantly. For example, patients with a Gleason score of 7 may have a 5-year BCR survival rate as low as 43%. Consequently, detecting BCR shortly after surgery may facilitate determining whether other treatments are necessary, and, if necessary, are initiated. Furthermore, Gleason scoring is subjective and is, therefore, susceptible to considerable inter-reviewer variability. Due to these limitations of Gleason scoring, other post-operative nomograms have been developed for predicting CaP aggressiveness and long-term post-surgical patient outcome.
Conventional methods for determining the extent and severity of cancer have involved a pathologist performing microscopic evaluation of histological images to determine a qualitative grade, e.g. a Gleason score. While conventional qualitative grading has been a valuable prognostic measure of aggressive disease, it suffers from inter-reviewer variability. The development of digital whole slide scanners has allowed the development of quantitative histomorphometry (QH). QH enables automated evaluation of histological tissue, creating accurate and repeatable analysis to attempt to overcome issues with inter-reviewer variability as it relates to quantifying disease appearance.
Conventional computerized QH algorithms for grading and diagnosing cancer have examined features based on co-occurrence matrices for the purpose of automated grading. Jafari-Khouzani et al., Multiwavelet grading of pathological images of prostate. IEEE Trans. On Biomedical Engineering, 50(6) (2003) 697-704. However, these matrices have been based on pixel intensity. Pixel intensity based matrices lack direct biological significance, and are thus less than optimal for diagnosing cancer. Conventional attempts to evaluate prostate histopathology in terms of grading have also looked at color, texture, and structural morphology. Tabesh et al., Multi-feature prostate cancer diagnosis and Gleason grading of histological images. IEEE Trans. on Medical Imaging 26(10) (2007) 1366-1378. However, these conventional methods do not investigate complex spatial relationships between structures.
Graph tessellations of cell nuclei using Voronoi or Delaunay graphs have been used to predict cancer grade. Christens-Barry and Partin, Quantitative grading of tissue and nuclei in prostate cancer for prognosis prediction, Johns Hopkins Apl. Technical Digest, 18:226-233, (1997). These graphs describe the spatial interactions between cell nuclei in the tissue of interest. However, the statistical features derived from Voronoi and Delaunay graphs are derived from fully connected graphs. This connecting of stromal and epithelial nuclei results in features extracted from conventional graphs representing averaged attributes of both stromal and epithelial regions. Unfortunately, such fully connected graphs are not sensitive to local variations in cell organization, and are therefore less than optimal in predicting BCR.
Local cell networks are difficult to quantify due to the variability of the size of the epithelial glands and stromal areas. Different types of cell graphs have been constructed to evaluate breast cancer in an alternative to Voronoi and Delaunay graphs. Bilgen, C. et al., Cell-graph mining for breast tissue modelling and classification. Engineering in Medicine and Biology Society, 2007, IEEE (2007) 5311-5314. While these graphs provide greater local constraints to separate stromal from epithelial regions, conventional graph mining does not include information pertaining to cell morphology.
Thus, while conventional methods for predicting BCR in prostate cancer patients have utilized QH, explored image textures, and employed fully-connected graphs and graph mining to assist in predicting CaP aggressiveness and BCR, conventional methods still suffer from the drawbacks of those techniques.